Dataset to evaluate the impact of environmental kernels in genomic prediction models

Integrating genomic and environmental information holds the potential for
enhancing the predictive power of genomic prediction models when
accounting for the genotype-by-environment interactions. Hence,
incorporating environmental covariates (EC) into these models can
significantly influence their predictive accuracy. In this study, we
utilized 1379 genotypes from the SoyNAM dataset, evaluated across four
environments and genotyped with 4611 single-nucleotide polymorphism
markers, to compare models incorporating genotype-by-environment and
genotype-by-environmental covariate interactions using different
covariance matrices. We evaluated four approaches: summarizing EC by
averaging (AVG), filtering ECs based on a coefficient of determination
criterion (FILT), segmenting ECs by crop phenology (STG), and a naïve
approach that utilized all available information (ALL). Predictive ability
was assessed as the Pearson correlation between the genomic estimated
breeding values and the adjusted phenotypes, considering 10 replicates of
three cross-validation scenarios (CV2: predicting tested genotypes in
observed environments; CV1: untested genotypes in observed environments;
CV0: tested genotypes in novel environments). Incorporating EC information
into the models increased average predictive ability from 0.42 to 0.56 for
CV1 and CV2. In these cases, the predictive ability was lower when EC
information was averaged to compute the environmental kinship matrix, with
slight differences observed with respect to the other approaches.
Regarding the CV0 scheme, the model incorporating only
genotype-by-environment information performed better (0.33). The naïve
method, which utilized all available EC information (ALL), proved to be a
promising approach, as it effectively improved the results in these
scenarios while eliminating the need for additional steps in selecting
variables.